MFSD12, transcriptionally regulated by PLAGL2, promotes bladder cancer progression

Bladder cancer (BLCA) is one of the most common malignant tumors of the urinary system. Identification of novel molecular signaling targets for the tumorigenesis of BLCA is important. Data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases suggest that major facilitator superfamily domain containing 12 (MFSD12) may act as an important oncogene in BLCA. MFSD12 expression is confirmed to be elevated in BLCA patients. Genetic manipulation of MFSD12 mediated by Tet-inducible lentiviral expression vector is conducted in two BLCA cell lines, including UMUC3 and 5637. Following this manipulation, the cells are subjected to treatment with or without doxycycline. Our results show that MFSD12 knockdown inhibits cell proliferation, migration, and invasion, and arrests the G1 stage-induced cell cycle. Furthermore, silencing of MFSD12 reduces lung metastatic lesions and xenografted tumor formation of BLCA cells. To further explore the effect of MFSD12 on BLCA cells, transcriptomics and metabolomics analyses are performed on MFSD12-overexpressing cells. Subsequently, luciferase reporters and chromatin immunoprecipitation (ChIP)-PCR assays reveal that MFSD12 is regulated positively by pleomorphic adenoma gene like-2 (PLAGL2), an important transcription factor. Collectively, our results indicate that MFSD12 exerts a tumor-promoting effect on BLCA progression, under the modulation of transcription factor PLAGL2.

Methods
For transcriptome analysis, UMUC3 cells infected with lentivirus overexpressing MFSD12 were treated with 1 μg/mL doxycycline (DOX) for 48 hours. RNA was extracted utilizing established protocols, and complementary DNA (cDNA) libraries underwent high-throughput sequencing via the Illumina Sequencing platform. Subsequently, these libraries were aligned to the GRCh38 reference genome, as provided by the Ensembl database, employing HISAT2 (version 2.2.1). The HTSeq software (version 2.0.2) was utilized to quantify read counts for each transcript. To identify differentially expressed genes (DEGs), DEseq2 software (version 1.28.0) was applied.

For metabonomics, metabolites were extracted by adding 1 mL of a solvent mixture comprising acetonitrile, methanol, and water in a volumetric ratio of 2:2:1. Subsequently, the metabolites were analyzed using a Thermo Q Exactive mass spectrometer in conjunction with a Thermo Vanquish ultra-high performance liquid chromatography system. The Proteowizard4 software (version 3.0.8789) facilitated the conversion of RAW files into .mzXML format, while XCMS software (version 3.12.0) was employed for peak alignment, filtering, and filling. Metabolite identification was conducted using various public databases. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed using the R "ropls" package (version 1.30.0), with differentially expressed metabolites (DEMs) identified based on a significance threshold of p < 0.05 and a variable importance in projection (VIP) score greater than 1.